LAUSR

Sleep apnea, characterized by breathing interruptions or slow breathing at night, can cause various health issues. Detecting respiratory rate (RR) using Wireless Fidelity (Wi-Fi) can identify sleep disorders without physical contact avoiding sleep disruption. However, traditional methods using Network Interface Cards (NICs) like the Intel Wi-Fi Link 5300 NIC are often costly and limited in channel state information (CSI) resolution. Our study introduces an effective strategy using the affordable ESP32 single-board computer for tracking RR through detailed analysis of Wi-Fi signal CSI. We developed a technique correlating Wi-Fi signal fluctuations with RR, employing signal processing methods—Hampel Filtering, Gaussian Filtering, Linear Interpolation, and Butterworth Low Pass Filtering—to accurately extract relevant signals. Additionally, noise from external movements is mitigated using a Z-Score for anomaly detection approach. We also implemented a local peak function to count peaks within an interval, scaling it to bpm for RR identification. RR measurements were conducted at different rates—Normal (12–16 bpm), Fast (>16 bpm), and Slow (<12 bpm)—to assess the effectiveness in both normal and sleep apnea conditions. Tested on data from 8 participants with distinct body types and genders, our approach demonstrated accuracy by comparing modeled sleep RR against actual RR measurements from the Vernier Respiration Monitor Belt. Optimal parameter settings yielded an overall average mean absolute deviation (MAD) of 2.60 bpm, providing the best result for normal breathing (MAD = 1.38). Different optimal settings were required for fast (MAD = 1.81) and slow breathing (MAD = 2.98). The results indicate that our method effectively detects RR using a low-cost approach under different parameter settings.